Chapter 5 Spatial R
We’ll explore the basics of simple features (sf) for building spatial datasets, then some common mapping methods, probably:
- ggplot2
- tmap
5.1 Spatial Data
To work with spatial data requires extending R to deal with it using packages. Many have been developed, but the field is starting to mature using international open GIS standards.
sp (until recently, the dominant library of spatial tools)
- Includes functions for working with spatial data
- Includes
spplotto create maps - Also needs
rgdalpackage forreadOGR– reads spatial data frames.
sf (Simple Features)
- ISO 19125 standard for GIS geometries
- Also has functions for working with spatial data, but clearer to use.
- Doesn’t need many additional packages, though you may still need
rgdalinstalled for some tools you want to use. - Replacing
spandspplotthough you’ll still find them in code. We’ll give it a try… - Works with ggplot2 and tmap for nice looking maps.
Cheat sheet: https://github.com/rstudio/cheatsheets/raw/master/sf.pdf
5.1.0.1 simple feature geometry sfg and simple feature column sfc
5.1.1 Examples of simple geometry building in sf
sf functions have the pattern st_*
st means “space and time”
See Geocomputation with R at https://geocompr.robinlovelace.net/ or https://r-spatial.github.io/sf/ for more details, but here’s an example of manual feature creation of sf geometries (sfg):
library(tidyverse)
library(sf)## Warning: package 'sf' was built under R version 4.0.4library(sf)
eyes <- st_multipoint(rbind(c(1,5), c(3,5)))
nose <- st_point(c(2,4))
mouth <- st_linestring(rbind(c(1,3),c(3, 3)))
border <- st_polygon(list(rbind(c(0,5), c(1,2), c(2,1), c(3,2),
c(4,5), c(3,7), c(1,7), c(0,5))))
face <- st_sfc(eyes, nose, mouth, border) # sfc = sf column
plot(face)
The face was a simple feature column (sfc) can be built from the list of sfgs. An sfc just has the one column, so not quite like a shapefile.
- But it can have a coordinate referencing system CRS, and so can be mapped.
- Kind of like a shapefile with no other attributes than shape
5.1.2 Building a mappable sfc from scratch
CA_matrix <- rbind(c(-124,42),c(-120,42),c(-120,39),c(-114.5,35),
c(-114.1,34.3),c(-114.6,32.7),c(-117,32.5),c(-118.5,34),c(-120.5,34.5),
c(-122,36.5),c(-121.8,36.8),c(-122,37),c(-122.4,37.3),c(-122.5,37.8),
c(-123,38),c(-123.7,39),c(-124,40),c(-124.4,40.5),c(-124,41),c(-124,42))
NV_matrix <- rbind(c(-120,42),c(-114,42),c(-114,36),c(-114.5,36),
c(-114.5,35),c(-120,39),c(-120,42))
CA_list <- list(CA_matrix); NV_list <- list(NV_matrix)
CA_poly <- st_polygon(CA_list); NV_poly <- st_polygon(NV_list)
sfc_2states <- st_sfc(CA_poly,NV_poly,crs=4326) # crs=4326 specifies GCS
st_geometry_type(sfc_2states)## [1] POLYGON POLYGON
## 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLElibrary(tidyverse)
ggplot() + geom_sf(data = sfc_2states)
sf class
Is like a shapefile: has attributes to which geometry is added, and can be used like a data frame.
attributes <- bind_rows(c(abb="CA", area=423970, pop=39.56e6),
c(abb="NV", area=286382, pop=3.03e6))
twostates <- st_sf(attributes, geometry = sfc_2states)
ggplot(twostates) + geom_sf() + geom_sf_text(aes(label = abb))## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
5.1.3 Creating features from shapefiles or tables
sf’s st_read reads shapefiles
- shapefile is an open GIS format for points, polylines, polygons
st_as_sf converts data frames
- using coordinates in data
library(tidyverse)
library(sf)
co <- st_read("data/BayAreaCounties.shp")## Reading layer `BayAreaCounties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaCounties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 60 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## Geodetic CRS: WGS 84freeways <- st_read("data/CAfreeways.shp")## Reading layer `CAfreeways' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\CAfreeways.shp' using driver `ESRI Shapefile'
## Simple feature collection with 72 features and 6 fields
## Geometry type: MULTILINESTRING
## Dimension: XY
## Bounding box: xmin: -124.2162 ymin: 32.54233 xmax: -114.4879 ymax: 42.00536
## Geodetic CRS: WGS 84censusBayArea <- st_read("data/BayAreaTracts.shp")## Reading layer `BayAreaTracts' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaTracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1663 features and 50 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## Geodetic CRS: WGS 84censusCentroids <- st_centroid(censusBayArea)
TRIdata <- read_csv("data/TRI_2017_CA.csv")
TRI_sp <- st_as_sf(TRIdata, coords = c("LONGITUDE", "LATITUDE"),
crs=4326) # simple way to specify coordinate reference
bnd <- st_bbox(censusCentroids)
ggplot() +
geom_sf(data = co, aes(fill = NAME)) +
geom_sf(data = censusCentroids) +
geom_sf(data = freeways, color = "grey") +
geom_sf(data = TRI_sp, color = "yellow") +
coord_sf(xlim = c(bnd[1], bnd[3]), ylim = c(bnd[2], bnd[4])) +
labs(title="Bay Area Counties, Freeways and Census Tract Centroids")
5.1.4 Coordinate Referencing System
Say you have data you need to make spatial with a spatial reference
sierra <- read_csv("sierraClimate.csv")
EPSG or CRS codes are an easy way to provide coordinate referencing.
Two ways of doing the same thing.
- Spell it out:
GCS <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
wsta = st_as_sf(sierra, coords = c("LONGITUDE","LATITUDE"), crs=GCS)- Google to find the code you need and assign it to the crs parameter:
wsta <- st_as_sf(sierra, coords = c("LONGITUDE","LATITUDE"), crs=4326)
5.1.4.1 Removing Geometry
There are many instances where you want to remove geometry from a sf data frame
Some R functions run into problems with geometry and produce confusing error messages, like “non-numeric argument”
You’re wanting to work with an sf data frame in a non-spatial way
What I’ve found as the best way to remove geometry:
myNonSFdf <- mySFdf %>% st_set_geometry(NULL)
5.1.5 Spatial join st_join
A spatial join with st_join joins data from census where TRI points occur
TRIdata <- read_csv("data/TRI_2017_CA.csv")## Warning: Missing column names filled in: 'X110' [110]## Warning: 3807 parsing failures.
## row col expected actual file
## 1 -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
## 2 -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
## 3 -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
## 4 -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
## 5 -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
## ... ... ........... ........... ......................
## See problems(...) for more details.TRI_sp <- st_as_sf(TRIdata, coords = c("LONGITUDE", "LATITUDE"),
crs=4326) %>%
st_join(censusBayArea) %>%
filter(CNTY_FIPS %in% c("013", "095"))5.1.6 Plotting maps in the base plot system
There are various programs for creating maps from spatial data, and we’ll look at a few after we’ve looked at rasters. As usual, the base plot system often does something useful when you give it data.
co <- st_read("data/BayAreaCounties.shp")## Reading layer `BayAreaCounties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaCounties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 60 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## Geodetic CRS: WGS 84plot(co)## Warning: plotting the first 9 out of 60 attributes; use max.plot = 60 to plot
## all
And with just one variable:
plot(co["POP_SQMI"])
There’s a lot more we could do with the base plot system, but we’ll mostly focus on some better options in ggplot2 and tmap.
5.2 Raster GIS in R
Simple Features are feature-based, so based on the name I guess it’s not surprising that sf doesn’t have support for rasters. But we can use the raster package for that.
A bit of raster reading and map algebra with Marble Mountains elevation data
library(raster)
elev <- raster("data/elev.tif")
slope <- terrain(elev, opt="slope")
aspect <- terrain(elev, opt="aspect")
slopeclasses <-matrix(c(0,0.2,1, 0.2,0.4,2, 0.4,0.6,3,
0.6,0.8,4, 0.8,1,5), ncol=3, byrow=TRUE)
slopeclass <- reclassify(slope, rcl = slopeclasses)
plot(elev)
plot(slope)
plot(slopeclass)
plot(aspect)
5.2.1 Raster from scratch
new_raster2 <- raster(nrows = 6, ncols = 6, res = 0.5,
xmn = -1.5, xmx = 1.5, ymn = -1.5, ymx = 1.5,
vals = 1:36)
plot(new_raster2)
5.3 ggplot2 for maps
The Grammar of Graphics is the gg of ggplot.
- Key concept is separating aesthetics from data
- Aesthetics can come from variables (using aes()setting) or be constant for the graph
Mapping tools that follow this lead
- ggplot, as we have seen, and it continues to be enhanced
- tmap (Thematic Maps) https://github.com/mtennekes/tmap Tennekes, M., 2018, tmap: Thematic Maps in R, Journal of Statistical Software 84(6), 1-39
CA <- st_read("data/CA_counties.shp")## Reading layer `CA_counties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\CA_counties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 58 features and 60 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -124.4152 ymin: 32.53427 xmax: -114.1312 ymax: 42.00952
## Geodetic CRS: WGS 84ggplot(CA) + geom_sf()
Try ?geom_sf and you’ll find that its first parameters is mapping with aes() by default. The data property is inherited from the ggplot call, but commonly you’ll want to specify data=something in your geom_sf call.
Another simple ggplot, with labels
CA <- st_read("data/CA_counties.shp")## Reading layer `CA_counties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\CA_counties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 58 features and 60 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -124.4152 ymin: 32.53427 xmax: -114.1312 ymax: 42.00952
## Geodetic CRS: WGS 84ggplot(CA) + geom_sf() +
geom_sf_text(aes(label = NAME), size = 1.5)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
and now with fill color
ggplot(CA) + geom_sf(aes(fill = MED_AGE)) +
geom_sf_text(aes(label = NAME), col="white", size=1.5)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Repositioned legend, no “x” or “y” labels
ggplot(CA) + geom_sf(aes(fill=MED_AGE)) +
geom_sf_text(aes(label = NAME), col="white", size=1.5) +
theme(legend.position = c(0.8, 0.8)) +
labs(x="",y="")
Map in ggplot2, zoomed into two counties:
library(tidyverse); library(sf)
co <- st_read("data/BayAreaCounties.shp")## Reading layer `BayAreaCounties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaCounties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 60 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## Geodetic CRS: WGS 84census <- st_read("data/BayAreaTracts.shp") %>%
filter(CNTY_FIPS %in% c("013", "095"))## Reading layer `BayAreaTracts' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaTracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1663 features and 50 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## Geodetic CRS: WGS 84TRI <- read_csv("data/TRI_2017_CA.csv") %>%
st_as_sf(coords = c("LONGITUDE", "LATITUDE"), crs=4326) %>%
st_join(census) %>%
filter(CNTY_FIPS %in% c("013", "095"),
(`5.1_FUGITIVE_AIR` + `5.2_STACK_AIR`) > 0)##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## TRI_FACILITY_ID = col_character(),
## FACILITY_NAME = col_character(),
## STREET_ADDRESS = col_character(),
## CITY = col_character(),
## COUNTY = col_character(),
## ST = col_character(),
## BIA_CODE = col_logical(),
## TRIBE = col_logical(),
## FEDERAL_FACILITY = col_character(),
## INDUSTRY_SECTOR = col_character(),
## PRIMARY_SIC = col_logical(),
## SIC_2 = col_logical(),
## SIC_3 = col_logical(),
## SIC_4 = col_logical(),
## SIC_5 = col_logical(),
## SIC_6 = col_logical(),
## CHEMICAL = col_character(),
## `CAS_#/COMPOUND_ID` = col_character(),
## CLEAR_AIR_ACT_CHEMICAL = col_character(),
## CLASSIFICATION = col_character()
## # ... with 8 more columns
## )
## i Use `spec()` for the full column specifications.## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planarbnd = st_bbox(census)
ggplot() +
geom_sf(data = co, aes(fill = NAME)) +
geom_sf(data = census, color="grey40", fill = NA) +
geom_sf(data = TRI) +
coord_sf(xlim = c(bnd[1], bnd[3]), ylim = c(bnd[2], bnd[4])) +
labs(title="Census Tracts and TRI air-release sites") +
theme(legend.position = "none")
5.3.1 Rasters in ggplot2
Raster display in ggplot2 is currently a little awkward, as are rasters in general in the feature-dominated GIS world.
We can use a trick: converting rasters to a grid of points:
library(tidyverse)
library(sf)
library(raster)
elevras <- raster("data/elev.tif") # note: the filename becomes a variable we'll use for fill
trails <- st_read("data/trails.shp")## Reading layer `trails' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\trails.shp' using driver `ESRI Shapefile'
## Simple feature collection with 32 features and 8 fields
## Geometry type: LINESTRING
## Dimension: XY
## Bounding box: xmin: 481903.8 ymin: 4599196 xmax: 486901.9 ymax: 4603200
## Projected CRS: NAD83 / UTM zone 10Nelevpts = as.data.frame(rasterToPoints(elevras))
ggplot() +
geom_raster(data = elevpts, aes(x = x, y = y, fill = elev)) +
geom_sf(data = trails)
5.4 tmap
Basic building block is tm_shape(data) followed by various layer elements such as tm_fill() shape can be features or raster See Geocomputation with R Chapter 8 “Making Maps with R” for more information. https://geocompr.robinlovelace.net/adv-map.html
library(spData)## Warning: package 'spData' was built under R version 4.0.4## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`##
## Attaching package: 'spData'## The following object is masked _by_ '.GlobalEnv':
##
## elevlibrary(tmap)## Warning: package 'tmap' was built under R version 4.0.4tm_shape(world) + tm_fill() + tm_borders()
Color by variable
library(sf)
library(tmap)
census <- st_read("data/BayAreaTracts.shp")## Reading layer `BayAreaTracts' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaTracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1663 features and 50 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## Geodetic CRS: WGS 84tm_shape(census) + tm_fill(col = "MED_AGE")
tmap of sierraFeb with hillshade and point symbols
library(tmap)
library(sf)
library(raster)
tmap_mode("plot")## tmap mode set to plottingtmap_options(max.categories = 8)
sierraFeb <- st_read("data/sierraFeb.csv")## Reading layer `sierraFeb' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\sierraFeb.csv' using driver `CSV'sierra <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)
hillsh <- raster("data/ca_hillsh_WGS84.tif")
bounds <- st_bbox(sierra)
tm_shape(hillsh,bbox=bounds)+
tm_raster(palette="-Greys",legend.show=FALSE,n=10) + tm_shape(sierra) + tm_symbols(col="TEMPERATURE",
palette=c("blue","red"), style="cont",n=8) +
tm_legend() +
tm_layout(legend.position=c("RIGHT","TOP"))## stars object downsampled to 1092 by 915 cells. See tm_shape manual (argument raster.downsample)
Note: “-Greys” needed to avoid negative image, since “Greys” go from light to dark, and to match reflectance as with b&w photography, they need to go from dark to light.
5.4.1 Interactive Maps
The word “static” in “static maps” isn’t something you would have heard in a cartography class 30 years ago, since essentially all maps then were static. Very important in designing maps is considering your audience, and one characteristic of the audience of those maps of yore were that they were printed and thus fixed on paper. A lot of cartographic design relates to that property:
- Figure-to-ground relationships assume “ground” is a white piece of paper (or possibly a standard white background in a pdf), so good cartographic color schemes tend to range from light for low values to dark for high values.
- Scale is fixed and there are no “tools” for changing scale, so a lot of attention must be paid to providing scale information.
- Similarly, without the ability to see the map at different scales, inset maps are often needed to provide context.
Interactive maps change the game in having tools for changing scale, and always being “printed” on a computer or device where the color of the background isn’t necessarily white. We are increasingly used to using interactive maps on our phones or other devices, and often get frustrated not being able to zoom into a static map.
A widely used interactive mapping system is Leaflet, but we’re going to use tmap to access Leaflet behind the scenes and allow us to create maps with one set of commands. The key parameter needed is tmap_mode which must be set to “view” to create an interactive map.
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(census) + tm_fill(col = "MED_AGE", alpha = 0.5)